Egg hatchability prediction by multiple linear regression and artificial neural networks
AUTOR(ES)
Bolzan, AC, Machado, RAF, Piaia, JCZ
FONTE
Revista Brasileira de Ciência Avícola
DATA DE PUBLICAÇÃO
2008-06
RESUMO
An artificial neural network (ANN) was compared with a multiple linear regression statistical method to predict hatchability in an artificial incubation process. A feedforward neural network architecture was applied. Network trainings were made by the backpropagation algorithm based on data obtained from industrial incubations. The ANN model was chosen as it produced data that fit better the experimental data as compared to the multiple linear regression model, which used coefficients determined by minimum square method. The proposed simulation results of these approaches indicate that this ANN can be used for incubation performance prediction.
Documentos Relacionados
- Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers
- ESTIMATING CO2 EMISSIONS FROM TILLED SOILS THROUGH ARTIFICIAL NEURAL NETWORKS AND MULTIPLE LINEAR REGRESSION1
- Prediction of Topsoil Texture Through Regression Trees and Multiple Linear Regressions
- ARTIFICIAL NEURAL NETWORKS FOR PREDICTION OF PHYSIOLOGICAL AND PRODUCTIVE VARIABLES OF BROILERS
- Prediction of Protein Functional Domains from Sequences Using Artificial Neural Networks